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      "source": [
        "from torchvision import datasets\n",
        "import torch\n",
        "data_folder = '~/data/FMNIST' # This can be any directory you want to \n",
        "# download FMNIST to\n",
        "fmnist = datasets.FashionMNIST(data_folder, download=True, train=True)\n",
        "tr_images = fmnist.data\n",
        "tr_targets = fmnist.targets"
      ],
      "execution_count": null,
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        {
          "output_type": "stream",
          "text": [
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw/train-images-idx3-ubyte.gz\n"
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          "text": [
            "Extracting /root/data/FMNIST/FashionMNIST/raw/train-images-idx3-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n"
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          "text": [
            "Extracting /root/data/FMNIST/FashionMNIST/raw/train-labels-idx1-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ],
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          "text": [
            "Extracting /root/data/FMNIST/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n",
            "\n",
            "\n"
          ],
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        },
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          "text": [
            "Extracting /root/data/FMNIST/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw\n",
            "Processing...\n",
            "Done!\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:469: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:141.)\n",
            "  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "65pVOnol3Eud"
      },
      "source": [
        "val_fmnist = datasets.FashionMNIST(data_folder, download=True, train=False)\n",
        "val_images = val_fmnist.data\n",
        "val_targets = val_fmnist.targets"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QpZxURIM3GQR"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import numpy as np\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UwnhSSRo3Hu8"
      },
      "source": [
        "class FMNISTDataset(Dataset):\n",
        "    def __init__(self, x, y):\n",
        "        x = x.float()/255\n",
        "        x = x.view(-1,28*28)\n",
        "        self.x, self.y = x, y \n",
        "    def __getitem__(self, ix):\n",
        "        x, y = self.x[ix], self.y[ix] \n",
        "        return x.to(device), y.to(device)\n",
        "    def __len__(self): \n",
        "        return len(self.x)\n",
        "\n",
        "from torch.optim import SGD, Adam\n",
        "def get_model():\n",
        "    model = nn.Sequential(\n",
        "        nn.Linear(28 * 28, 1000),\n",
        "        nn.ReLU(),\n",
        "        nn.Linear(1000, 10)\n",
        "    ).to(device)\n",
        "\n",
        "    loss_fn = nn.CrossEntropyLoss()\n",
        "    optimizer = Adam(model.parameters(), lr=1e-2)\n",
        "    return model, loss_fn, optimizer\n",
        "\n",
        "def train_batch(x, y, model, opt, loss_fn):\n",
        "    model.train()\n",
        "    prediction = model(x)\n",
        "    batch_loss = loss_fn(prediction, y)\n",
        "    batch_loss.backward()\n",
        "    optimizer.step()\n",
        "    optimizer.zero_grad()\n",
        "    return batch_loss.item()\n",
        "\n",
        "def accuracy(x, y, model):\n",
        "    model.eval()\n",
        "    # this is the same as @torch.no_grad \n",
        "    # at the top of function, only difference\n",
        "    # being, grad is not computed in the with scope\n",
        "    with torch.no_grad():\n",
        "        prediction = model(x)\n",
        "    max_values, argmaxes = prediction.max(-1)\n",
        "    is_correct = argmaxes == y\n",
        "    return is_correct.cpu().numpy().tolist()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zC1KAaEv3QiZ"
      },
      "source": [
        "def get_data(): \n",
        "    train = FMNISTDataset(tr_images, tr_targets) \n",
        "    trn_dl = DataLoader(train, batch_size=32, shuffle=True)\n",
        "    val = FMNISTDataset(val_images, val_targets) \n",
        "    val_dl = DataLoader(val, batch_size=len(val_images), shuffle=False)\n",
        "    return trn_dl, val_dl"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z55H-eMO3R3V"
      },
      "source": [
        "@torch.no_grad()\n",
        "def val_loss(x, y, model):\n",
        "    prediction = model(x)\n",
        "    val_loss = loss_fn(prediction, y)\n",
        "    return val_loss.item()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GOYt6Mtv3bCu"
      },
      "source": [
        "trn_dl, val_dl = get_data()\n",
        "model, loss_fn, optimizer = get_model()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tH46l-zA3cec",
        "outputId": "fb6b829d-6215-4a10-8ebe-b3b4c786ddc7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        }
      },
      "source": [
        "train_losses, train_accuracies = [], []\n",
        "val_losses, val_accuracies = [], []\n",
        "for epoch in range(5):\n",
        "    print(epoch)\n",
        "    train_epoch_losses, train_epoch_accuracies = [], []\n",
        "    for ix, batch in enumerate(iter(trn_dl)):\n",
        "        x, y = batch\n",
        "        batch_loss = train_batch(x, y, model, optimizer, loss_fn)\n",
        "        train_epoch_losses.append(batch_loss) \n",
        "    train_epoch_loss = np.array(train_epoch_losses).mean()\n",
        "\n",
        "    for ix, batch in enumerate(iter(trn_dl)):\n",
        "        x, y = batch\n",
        "        is_correct = accuracy(x, y, model)\n",
        "        train_epoch_accuracies.extend(is_correct)\n",
        "    train_epoch_accuracy = np.mean(train_epoch_accuracies)\n",
        "    for ix, batch in enumerate(iter(val_dl)):\n",
        "        x, y = batch\n",
        "        val_is_correct = accuracy(x, y, model)\n",
        "        validation_loss = val_loss(x, y, model)\n",
        "    val_epoch_accuracy = np.mean(val_is_correct)\n",
        "    train_losses.append(train_epoch_loss)\n",
        "    train_accuracies.append(train_epoch_accuracy)\n",
        "    val_losses.append(validation_loss)\n",
        "    val_accuracies.append(val_epoch_accuracy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n",
            "1\n",
            "2\n",
            "3\n",
            "4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QorP8HZP3pi0",
        "outputId": "9a53c3e2-b193-42de-e57a-aa1c28b6ec6e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        }
      },
      "source": [
        "epochs = np.arange(5)+1\n",
        "import matplotlib.ticker as mtick\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.ticker as mticker\n",
        "%matplotlib inline\n",
        "plt.subplot(211)\n",
        "plt.plot(epochs, train_losses, 'bo', label='Training loss')\n",
        "plt.plot(epochs, val_losses, 'r', label='Validation loss')\n",
        "plt.gca().xaxis.set_major_locator(mticker.MultipleLocator(1))\n",
        "plt.title('Training and validation loss when batch size is 32')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.grid('off')\n",
        "plt.show()\n",
        "plt.subplot(212)\n",
        "plt.plot(epochs, train_accuracies, 'bo', label='Training accuracy')\n",
        "plt.plot(epochs, val_accuracies, 'r', label='Validation accuracy')\n",
        "plt.gca().xaxis.set_major_locator(mticker.MultipleLocator(1))\n",
        "plt.title('Training and validation accuracy when batch size is 32')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.gca().set_yticklabels(['{:.0f}%'.format(x*100) for x in plt.gca().get_yticks()]) \n",
        "plt.legend()\n",
        "plt.grid('off')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Ky6u6z54Blo"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XwkgtaFW4GVP"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "auquZOA14GXd"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9qMLWAQy4Gw2"
      },
      "source": [
        "Batch size of 10000"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yIcsIrtU4Irw"
      },
      "source": [
        "def get_data(): \n",
        "    train = FMNISTDataset(tr_images, tr_targets) \n",
        "    trn_dl = DataLoader(train, batch_size=10000, shuffle=True)\n",
        "    val = FMNISTDataset(val_images, val_targets) \n",
        "    val_dl = DataLoader(val, batch_size=len(val_images), shuffle=False)\n",
        "    return trn_dl, val_dl"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZGEBAfe14KmD"
      },
      "source": [
        "trn_dl, val_dl = get_data()\n",
        "model, loss_fn, optimizer = get_model()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GC7DOpGD4OSs",
        "outputId": "90a6f725-497e-4290-b1a7-110446b32386",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        }
      },
      "source": [
        "train_losses, train_accuracies = [], []\n",
        "val_losses, val_accuracies = [], []\n",
        "for epoch in range(5):\n",
        "    print(epoch)\n",
        "    train_epoch_losses, train_epoch_accuracies = [], []\n",
        "    for ix, batch in enumerate(iter(trn_dl)):\n",
        "        x, y = batch\n",
        "        batch_loss = train_batch(x, y, model, optimizer, loss_fn)\n",
        "        train_epoch_losses.append(batch_loss) \n",
        "    train_epoch_loss = np.array(train_epoch_losses).mean()\n",
        "\n",
        "    for ix, batch in enumerate(iter(trn_dl)):\n",
        "        x, y = batch\n",
        "        is_correct = accuracy(x, y, model)\n",
        "        train_epoch_accuracies.extend(is_correct)\n",
        "    train_epoch_accuracy = np.mean(train_epoch_accuracies)\n",
        "    for ix, batch in enumerate(iter(val_dl)):\n",
        "        x, y = batch\n",
        "        val_is_correct = accuracy(x, y, model)\n",
        "        validation_loss = val_loss(x, y, model)\n",
        "    val_epoch_accuracy = np.mean(val_is_correct)\n",
        "    train_losses.append(train_epoch_loss)\n",
        "    train_accuracies.append(train_epoch_accuracy)\n",
        "    val_losses.append(validation_loss)\n",
        "    val_accuracies.append(val_epoch_accuracy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n",
            "1\n",
            "2\n",
            "3\n",
            "4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AttqfaY44QMK",
        "outputId": "de5d2588-7866-4621-b355-251b3dcabdbc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        }
      },
      "source": [
        "epochs = np.arange(5)+1\n",
        "import matplotlib.ticker as mtick\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.ticker as mticker\n",
        "%matplotlib inline\n",
        "plt.subplot(211)\n",
        "plt.plot(epochs, train_losses, 'bo', label='Training loss')\n",
        "plt.plot(epochs, val_losses, 'r', label='Validation loss')\n",
        "plt.gca().xaxis.set_major_locator(mticker.MultipleLocator(1))\n",
        "plt.title('Training and validation loss when batch size is 10000')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.grid('off')\n",
        "plt.show()\n",
        "plt.subplot(212)\n",
        "plt.plot(epochs, train_accuracies, 'bo', label='Training accuracy')\n",
        "plt.plot(epochs, val_accuracies, 'r', label='Validation accuracy')\n",
        "plt.gca().xaxis.set_major_locator(mticker.MultipleLocator(1))\n",
        "plt.title('Training and validation accuracy when batch size is 10000')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.gca().set_yticklabels(['{:.0f}%'.format(x*100) for x in plt.gca().get_yticks()]) \n",
        "plt.legend()\n",
        "plt.grid('off')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FEoyYJqx4X3i"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}